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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: dccuchile/distilbert-base-spanish-uncased
model-index:
- name: custom-ner-model2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# custom-ner-model2
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2050
- Precision: 0.8542
- Recall: 0.8817
- F1: 0.8677
- Accuracy: 0.9595
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 105 | 0.5185 | 0.5840 | 0.5484 | 0.5656 | 0.8596 |
| No log | 2.0 | 210 | 0.3212 | 0.7365 | 0.7312 | 0.7338 | 0.9050 |
| No log | 3.0 | 315 | 0.2440 | 0.8123 | 0.8065 | 0.8094 | 0.9389 |
| No log | 4.0 | 420 | 0.2186 | 0.8014 | 0.8100 | 0.8057 | 0.9431 |
| 0.4107 | 5.0 | 525 | 0.1911 | 0.8481 | 0.8602 | 0.8541 | 0.9516 |
| 0.4107 | 6.0 | 630 | 0.1931 | 0.8235 | 0.8530 | 0.8380 | 0.9546 |
| 0.4107 | 7.0 | 735 | 0.1720 | 0.8368 | 0.8638 | 0.8501 | 0.9570 |
| 0.4107 | 8.0 | 840 | 0.1858 | 0.8385 | 0.8746 | 0.8561 | 0.9583 |
| 0.4107 | 9.0 | 945 | 0.1858 | 0.85 | 0.8530 | 0.8515 | 0.9552 |
| 0.0667 | 10.0 | 1050 | 0.1961 | 0.8526 | 0.8710 | 0.8617 | 0.9564 |
| 0.0667 | 11.0 | 1155 | 0.1970 | 0.8537 | 0.8781 | 0.8657 | 0.9589 |
| 0.0667 | 12.0 | 1260 | 0.1865 | 0.8478 | 0.8781 | 0.8627 | 0.9619 |
| 0.0667 | 13.0 | 1365 | 0.1994 | 0.8379 | 0.8710 | 0.8541 | 0.9583 |
| 0.0667 | 14.0 | 1470 | 0.1913 | 0.8507 | 0.8781 | 0.8642 | 0.9613 |
| 0.0274 | 15.0 | 1575 | 0.2064 | 0.8512 | 0.8817 | 0.8662 | 0.9595 |
| 0.0274 | 16.0 | 1680 | 0.2053 | 0.8478 | 0.8781 | 0.8627 | 0.9601 |
| 0.0274 | 17.0 | 1785 | 0.2037 | 0.8576 | 0.8853 | 0.8713 | 0.9601 |
| 0.0274 | 18.0 | 1890 | 0.2056 | 0.8632 | 0.8817 | 0.8723 | 0.9595 |
| 0.0274 | 19.0 | 1995 | 0.2066 | 0.8571 | 0.8817 | 0.8693 | 0.9601 |
| 0.0162 | 20.0 | 2100 | 0.2050 | 0.8542 | 0.8817 | 0.8677 | 0.9595 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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